The present embodiments relate to a method for accelerating non-rigid image registration, using parallel processors. Image registration is a task that is significant in medical image processing. Because of the many possibilities in examination, medical images can be made at different times or using different equipment. Combining these images can help the physician substantially in diagnosis. However, this requires geometrical 1:1 correspondence of the objects to be assessed. The task of image registration is therefore to determine this correspondence.
Image registration is used in digital subtraction angiography (DSA). DSA is a currently used method for visualizing vessels. A “mask image”—a digital X-ray image prior to the administration of contrast agent—is first made. Then, the radio contrast agent is injected and “contrast images” are taken—a series of successive images in which the vessels are filled with contrast agent. In the next step, the mask image is subtracted from each of the contrast images, so that, by the end, only the target structures—that is, the contrast agent-filled vessels—are shown. The resultant differential images, however, are diagnostically conclusive only if the structures in the two input images are oriented relative to one another.
Since the images are taken at different times, there is often a 3-dimensional change in the structures, usually from patient movement. Although many principles for avoiding patient motion before and during the scanning are available, nevertheless not all kinds of motion can be prevented, such as heartbeats, the urge to cough, or swallowing. Reverse registration of the two images that are to be subtracted is therefore necessary. A transformation is determined that describes the motion mathematically. The geometrical transformation that takes place substantially comprises translational motions, but also slight rotations, scaling, and even deformations also occur. As a consequence, rigid registration typically does not suffice. Although non-rigid methods provide markedly better results, still they entail major computation expense.
One known example of the non-rigid methods for motion correction is block matching. The displacement vector of a pixel is determined approximately by optimizing the similarity between the two blocks that, in the respective image, surround the pixel (FIG. 2). To achieve precise registration results, there is a need for robust similarity standards. Because of the inflow of contrast agent, a change in the average intensity level exists in the two frames. The similarity standards that analyze the intensity difference directly are unsuited to DSA. Conversely, histogram-based similarity standards do not determine the correspondence with the actual intensity disparities, but rather with the relative frequency of disparities. In “Image Enhancement in Digital X-Ray Angiography” by E. Meijering, PhD thesis, Image Sciences Institute, Utrecht University, 2000, it is shown that such similarity standards are intrinsically robust with respect to the inflow of contrast agent.
It is known that calculating histograms is computation-intensive. Consequently, the entire algorithm is not efficient enough to make the registered images available to the physician immediately after the images have been made.